Sr. Marketing Mgr
Have worked in the Semiconductor Industry for close to 24yrs. in a number of management roles. He started at Xilinx for 3.5yrs ago in the traditional Broadcast group and was responsible for North American market . A year and a half ago he branched off with Johan Janssen (Chief Video Architect) to build Xilinx’s Cloud Video strategy. During this time they have built out a best-of-breed ecosystem, led Xilinx to make a number of strategic investments and won a number of Tier 1 customers. Prior to Xilinx, Sean worked at Texas Instruments in a number of management roles. He formed their Video Infrastructure group which was responsible for generating significant revenue and success TI had with their DaVinci family of video SOCs. Sean was also the worldwide marketing manager for TI’s Video Analytics product line which was the world’s first family of vision focused processors. Sean started his career in Canada and spent a number of years at a number of small Canadian companies with Gennum (now part of Semtech) the inventor of the SDI video transport protocol being one of the more significant.
Live video traffic is growing faster than any other video traffic type and China’s video operators are witnessing this first hand. The volume of traffic shows no signs of slowing down and this is putting pressure on existing infrastructure and associated financial models that providers have relied on since live streaming’s inception. The industry is desperate for a new approach that will enable lower bandwidth requirements, reduced infrastructure costs while simultaneously maintaining the agility that software has provided. Field Programmable Gate Arrays or FPGAs can address these needs and have most recently gained popularity in leading Live Streaming applications. Hardware acceleration for live streaming is very attractive but companies has existing implementations and software that they cannot disrupt or change easily. During this talk Xilinx will highlight will outline how FPGAs can be used in existing applications and future requirements like Video + Machine Learning. We Xilinx has integrated with FFmpeg and engineers can utilize FPGAs in their existing networks without having to make significant changes to their software infrastructure. We will also demonstrate how Machine Learning can be integrated In to FFmpeg enabling acceleration all through command line interface.